With the rapid development and advancement of cars and other vehicles in urban transportation, autonomous driving has attracted more attention in the last decades. The cameras assembled on the self-driving cars capture the real-time stream of videos that need to be processed with online algorithms. The car should be aware of the surrounding environment and spot road users, including pedestrians, cyclists, and other vehicles, to make safe autonomous decisions. Also, it should be able to detect and anticipate road users activities such as moving away, moving towards, crossing the road, and anomalous events in real-time to adjust the speed and handle the situation. Therefore, spatio-temporal action localization algorithms need to be developed to guarantee the safety of self-driving cars [1]}. Yao et al. [2]} proposed a traffic anomaly detection with a when-where-what pipeline to detect, localize, and recognize anomalous events from egocentric videos. To improve the detection and prediction of pedestrian movements, Rasouli et al. [3]} studied pedestrian behavior depending on various factors, such as demographics of the pedestrians, traffic dynamics, and environmental conditions. Moreover, Mahadevan et al. [4]} proposed an immersive VR-based pedestrian mixed traffic simulator to examine pedestrian behavior in street crossing tasks.
